{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A straight waveguide\n",
    "For our first example, let's examine the field pattern excited by a localized continuous wave (CW) source in a waveguide — first straight, then bent. The waveguide will have frequency-independent $\\epsilon$=12 and width 1 $\\mu$m. The unit length in this example is 1 $\\mu$m. See also Units.\n",
    "\n",
    "The first thing to do always is to load the Meep library, along with any other library we may need for post processing or visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using MPI version 3.1, 1 processes\n"
     ]
    }
   ],
   "source": [
    "import meep as mp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Domain\n",
    "\n",
    "We can begin specifying each of the simulation objects starting with the computational cell. \n",
    "\n",
    "We're going to put a source at one end and watch the fields propagate down the waveguide in the $x$ direction, so let's use a cell of length 16 $\\mu$m in the $x$ direction to give it some distance to propagate. In the $y$ direction, we just need enough room so that the boundaries do not affect the waveguide mode; let's give it a size of 8 $\\mu$m."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell = mp.Vector3(16,8,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Vector3` object stores the size of the cell in each of the three coordinate directions. This is a 2d cell in $x$ and $y$ where the $z$ direction has size 0. All 2D simulations must lie in the XY plane, as we've specified."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geometry\n",
    "Next we add the waveguide. Most commonly, the device structure is specified by a set of `GeometricObjects` stored in the `geometry` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "geometry = [mp.Block(mp.Vector3(mp.inf,1,mp.inf),\n",
    "                     center=mp.Vector3(),\n",
    "                     material=mp.Medium(epsilon=12))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The waveguide is specified by a Block (parallelepiped) of size $\\infty \\times 1 \\times \\infty$, with $\\epsilon$=12, centered at (0,0) which is the center of the cell. By default, any place where there are no objects there is air ($\\epsilon$=1), although this can be changed by setting the `default_material` variable (shown below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sources\n",
    "We have the structure and need to specify the current sources using the `sources` object. The simplest thing is to add a single point source $J_z$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sources = [mp.Source(mp.ContinuousSource(frequency=0.15),\n",
    "                     component=mp.Ez,\n",
    "                     center=mp.Vector3(-7,0))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We gave the source a frequency of 0.15, and specified a `ContinuousSource` which is just a fixed-frequency sinusoid exp($−i\\omega t$) that by default is turned on at $t=0$. \n",
    "\n",
    "Recall that, in Meep units, frequency is specified in units of $2\\pi c$, which is equivalent to the inverse of the vacuum wavelength. Thus, 0.15 corresponds to a vacuum wavelength of about 1/0.15=6.67 μm, or a wavelength of about 2 μm in the ε=12 material — thus, our waveguide is half a wavelength wide, which should hopefully make it single mode. In fact, the cutoff for single-mode behavior in this waveguide is analytically solvable, and corresponds to a frequency of $\\frac{1}{2\\sqrt{11}}$ or roughly 0.15076. \n",
    "\n",
    "Note also that to specify a $J_z$, we specify a component `Ez` (e.g., if we wanted a magnetic current, we would specify `Hx`, `Hy`, or `Hz`). \n",
    "\n",
    "The current is located at (-7,0), which is 1 $\\mu$m to the right of the left edge of the cell — we always want to leave a little space between sources and the cell boundaries, to keep the boundary conditions from interfering with them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boundary layers\n",
    "As for boundary conditions, we want to add absorbing boundaries around our cell. \n",
    "\n",
    "Absorbing boundaries in Meep are handled by perfectly matched layers (PML) — which aren't really a boundary condition at all, but rather a fictitious absorbing material added around the edges of the cell. To add an absorbing layer of thickness 1 $\\mu$m around all sides of the cell, we do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pml_layers = [mp.PML(1.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pml_layers` is a `list` of `PML` objects — you may have more than one `PML` object if you want `PML` layers only on certain sides of the cell. For example, `mp.PML(thickness=1.0,direction=mp.X,side=mp.high)` specifies a `PML` layer on only the `+x` side. \n",
    "\n",
    "An important point: the PML layer is inside the cell, overlapping whatever objects you have there. So, in this case our PML overlaps our waveguide, which is what we want so that it will properly absorb waveguide modes. \n",
    "\n",
    "The finite thickness of the PML is important to reduce numerical reflections. For more information, see Perfectly Matched Layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resolution\n",
    "Meep will discretize this structure in space and time, and that is specified by a single variable, `resolution`, that gives the number of pixels per distance unit. \n",
    "\n",
    "We'll set this resolution to 10 pixels/μm, which corresponds to around 67 pixels/wavelength, or around 20 pixels/wavelength in the high-index material. In general, at least 8 pixels/wavelength in the highest dielectric is a good idea. This will give us a 160×80 cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "resolution = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Object\n",
    "\n",
    "The final object to specify is `Simulation` which is based on all the previously defined objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = mp.Simulation(cell_size=cell,\n",
    "                    boundary_layers=pml_layers,\n",
    "                    geometry=geometry,\n",
    "                    sources=sources,\n",
    "                    resolution=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "Before we actually run our simulation, we often want to visualize the entire domain to ensure that the geometry, source, boundary layers, and even monitors are all correct.\n",
    "\n",
    "We can create a figure using `matplotlib`, and pass the figure axis to the `plot2D` function. This function plots all of the relevant simulation objects in the passed axis handle. Any arbitrary slice may be specified. The default is through `z=0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "     block, center = (0,0,0)\n",
      "          size (1e+20,1,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(dpi=100)\n",
    "sim.plot2D()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The green boxes represent the PML boundary layers. The black line through the middle is our waveguide. The red dot is our source.\n",
    "\n",
    "Everything looks like we would expect, so let's run the simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate\n",
    "We are ready to run the simulation. We time step the fields until a time of 200:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run 0 finished at t = 200.0 (4000 timesteps)\n"
     ]
    }
   ],
   "source": [
    "sim.run(until=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It should finish in less than a second. We can analyze and visualize the fields using `plot2D`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "sim.plot2D(fields=mp.Ez)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the `Ez` component of the fields superimposed on the previous domain plot. We can choose to plot any of the six field components specified earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Animation\n",
    "Often, we want to track the evolution of the fields as a function of time. This helps us ensure the fields are propogating as we would expect.\n",
    "\n",
    "We can easily accomplish this using a `run` function. Run functions are passed to the `sim.run()` method and can be called every time step. The `Animate2D()` run function can be used to generate an animation object by grabbing frames from an arbitrary number of time steps.\n",
    "\n",
    "We need to pass the `sim` object we created, specify which `fields` component we are interested in tracking, specify how often we want to record the fields, and whether to plot everything in real time. For this simulation, let's look at the `Ez` fields and take a snapshot every 1 time units. \n",
    "\n",
    "Unfortunately, Jupyter notebooks don't render realtime figure updates natively, so we'll turn this feature off and just view the animation after it runs.\n",
    "\n",
    "We also want to restart the field propogation by calling `reset_sim()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.reset_meep()\n",
    "f = plt.figure(dpi=100)\n",
    "Animate = mp.Animate2D(sim, fields=mp.Ez, f=f, realtime=False, normalize=True)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once defined, we can pass it along with our original argument, `until`. This time, however, we'll just run until 50 time units. We'll tell meep to record the field information every 1 time unit by using the `at_every()` run function modifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "     block, center = (0,0,0)\n",
      "          size (1e+20,1,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "Normalizing field data...\n",
      "run 1 finished at t = 100.0 (2000 timesteps)\n"
     ]
    }
   ],
   "source": [
    "sim.run(mp.at_every(1,Animate),until=100)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've run the simulation, we can postprocess the animation and export it to an mp4 video using the `to_mp4()` method. We'll specify a filename and 10 frames-per-second (`fps`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating MP4...\n"
     ]
    }
   ],
   "source": [
    "filename = \"media/straight_waveguide.mp4\"\n",
    "Animate.to_mp4(10,filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can use some iPython tools to visualize the animation natively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"media/straight_waveguide.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Video\n",
    "Video(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the fields propogate down the waveguide."
   ]
  }
 ],
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